PREREQ is a supervised method for inferring prerequisite relations between educational concepts, introduced by Roy, Madhyastha, Lawrence, and Rajan at IAAI-19 (AAAI 2019). Concepts are represented by latent topic vectors learned with Pairwise Latent Dirichlet Allocation (Pairwise-LDA) over concept-aligned online educational text (e.g., course materials and Wikipedia), and a Siamese neural network then scores an ordered pair of concept representations to predict whether the first concept is a prerequisite of the second. The method targets the asymmetric, directed nature of prerequisite links and is evaluated on online-course and university-course concept datasets, where it outperforms prior unsupervised and supervised baselines. The work is one of the standard references in the subsequent prerequisite-learning literature alongside Liang et al. (2015) and the LectureBank line.